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Question about st.cache_data TTL and Memory Cleanup

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首先查看 issue 中引用的 Streamlit cache_data TTL 和 cache management 文档，然后追踪文档所述的过期条目行为和内存清理。确认文档是否解释了后台过期、保留的 Python 内存以及按条目回收；完成的标准是添加清晰且有依据的说明，或确定适当的文档缺口。

由索引模型根据 Issue 内容生成。

描述

docs needs triage

Hi Streamlit Team,

I have a question about how st.cache_data works with ttl, specifically around memory cleanup.

For example:

@st.cache_data(ttl=3600)
def load_data():
...

Scenario
At 10:00 AM, User 1 calls the function.

The function caches a DataFrame of about 1 GB.

The cache TTL is 1 hour.

No one calls this function again until 4:00 PM.

What I observed
While monitoring my application’s memory usage, I noticed that even though the TTL had expired several hours earlier, the process memory did not decrease. For example, after the cached DataFrame increased the application’s memory to around 1 GB, the memory usage stayed close to 1 GB instead of dropping after the TTL expired.

This made me wonder:

Is this the expected behavior?

Does Streamlit have any background process that automatically removes expired cache entries from memory, or are expired entries only cleaned up when the cached function is called again?

If a cache entry has expired, is it removed from Streamlit’s cache while the underlying Python memory is still retained? If so, is there a recommended way to release or reclaim that memory?

I looked into the available cache management options, including:

max_entries

st.cache_data.clear()

function_name.clear()

However, none of these seem ideal for my use case.

Using max_entries across many cached functions would increase cache evictions, leading to more cache misses and recomputation, which impacts page performance.

Similarly, st.cache_data.clear() or function_name.clear() removes all cached entries (globally or for a function), which isn’t practical in a production application where other users may still be relying on those cached results.

Is there a recommended approach for releasing memory used by expired cache entries without clearing the entire cache or aggressively limiting the cache size?

I also tested the behavior with a second user.

At 4:00 PM, User 2 called the same cached function with different input parameters.

The function created a new cached result of approximately 800 MB.

I expected the old cache entry to be released and the application’s memory usage to settle around 800 MB. Instead, the process memory increased from about 1 GB to approximately 1.8 GB, which suggests that the earlier cached data was still occupying memory while the new cache entry was added.

Is this also the expected behavior? If so, when is the memory used by an expired cache entry actually released?

I’d appreciate any clarification on how TTL expiration and memory management are intended to work internally.

Thank you!

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